Key Responsibilities: Design, develop, and maintain scalable data pipelines for ingestion, transformation, and delivery of structured and unstructured data. Collaborate directly with traders, quantitative analysts, and portfolio managers to understand data needs and deliver actionable insights. Integrate and manage real-time market data feeds (e.g., Bloomberg, ICE, CME) and internal trading systems. Build and optimize data models for pricing, risk, and PnL analytics. Ensure high availability and performance of data infrastructure in a low-latency trading environment. Implement data quality checks, monitoring, and alerting systems. Work closely with DevOps and infrastructure teams to deploy solutions in cloud and on-prem environments. Required Skills & Experience Strong proficiency in Python, including libraries such as Pandas, NumPy, and PySpark. Experience with data engineering tools (e.g., Airflow, Kafka, SQL, Parquet). Solid understanding of commodities markets, trading workflows, and financial instruments. Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization (Docker, Kubernetes). Proven ability to work in fast-paced, high-pressure environments with direct exposure to the Front Office. Excellent communication skills and ability to translate technical solutions into business value.